Sorting Lines Research Articles

Evaluation of sustainable waste management: An analysis of techno-economic and life cycle assessments of municipal solid waste sorting and decontamination

This study evaluates the economic and environmental feasibility of Municipal Solid Waste (MSW) sorting and decontamination technologies across urban, suburban, and rural areas. Using Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), the research assesses cost-effectiveness and environmental impacts, with a focus on cost variability analyzed through Monte Carlo simulations. Findings indicate significant cost differences based on population density: rural areas incur high costs up to $764/ton due to low waste volumes and limited infrastructure, whereas suburban and urban areas have more feasible costs ranging from $36.3 to $142.5/ton. Environmental impacts also vary, with greenhouse gas emissions at 171 kg CO2 eq/ton for copy paper and 118.6 kg CO2 eq/ton for plastics. PM2.5 levels are 9.1 g/ton for copy paper and 6.3 g/ton for plastics, with sorting lines being the main contributors. Monte Carlo simulations reveal a 50% probability of costs being below $102.26/ton for copy paper and $115.8/ton for plastics in suburban settings. The study underscores the importance of customized waste management strategies to improve economic viability and sustainability based on local conditions.

Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line

Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line.

Decision-Making Model in Information Technology Management of Logistics Companies

The research is devoted to developing an innovative decision-making model for automating the sorting processes of postal item sorting. In today’s environment of rapid development of logistics services and growing volumes of mail, there is a need to improve existing technologies and methods of managing sorting lines. The main goal of this study is to create an effective model that will ensure the optimisation of sorting terminals with loading points. The purpose of the research is to develop decision-making models for managing postal service lines that implement sorting logic for terminals with loading points. The research methodology is based on a systematic approach that allows us to take into account all aspects of sorting lines. The theories of finite predicate algebra and set algebra were used to create mathematical sorting models. Methods of building decision support systems and modern information technologies ensure the integration of the developed algorithms into real postal systems. The scientific novelty lies in defining and implementing a variant of the decision-making model for an automated sorting line for mail. The model implements the specified sorting logic, which makes it possible to significantly improve the efficiency and accuracy of the processes of loading and sorting mail. Conclusions. A variant of implementing the model of the decision support system for an automated sorting line of mail is proposed. The developed model makes it possible to implement the logic of sorting mail for terminals with defined loading points. This helps to increase the efficiency and reliability of postal services, reducing the likelihood of errors and ensuring timely delivery.

Excluding sunscald from long-term storage of ‘Granny Smith’ apples

Sunscald is a postharvest apple and pear peel disorder primarily caused by excessive light exposure during the growing season. Symptoms are not apparent at-harvest but develop during storage as superficial brown or gray discolored patches on the exposed portion of the apple. A novel protocol using hyperspectral imaging (HSI) was developed to identify apples at- harvest that are more likely to develop the disorder and reduce sunscald in the cold chain by eliminating at-risk apples from long-term storage. PLS-DA and LOGISTIC models and an index summarizing the chlorophyll to carotenoid ratio were tested for this purpose using two populations selected for different sun exposure levels and four populations harvested from whole trees. The accuracy of models varied seasonally. However, detection of sunburn and estimation of sunscald risk based on index values was a more consistent approach. Summarizing peel area according to this index was the basis for categorizing whole apples as rejected (sunburned), high (sunscald) risk, or low-risk. Categorization segregated apples nearly entirely into these categories with only an insignificant frequency of misclassification. The frequency of high-risk apples without sunburn symptoms in the populations harvested from whole trees fell below 20 %, although actual sunscald incidence within this category ranged from 55 % to 81 % among populations harvested from whole trees, highlighting that other factors beyond elevated sun exposure contribute to sunscald incidence. The classification model for low and high-risk categories had an accuracy of 83–93 % in four independent populations, with 99.9 % sensitivity to classify non-sunscalded fruit into the low-risk category. Sorting by sunburn presence and sunscald risk allows for apples that have sunburn symptoms to be excluded and those that are marketable but most likely to develop sunscald to be pushed to market rather than storage. With the appropriate source/sensor array, this approach is readily adaptable to sorting lines or even robotic harvesters. Challenges include adapting this approach for other apple and pear cultivars and extending capabilities for additional uses, including sorting fruit into additional categories representative of relative sunlight exposure.

Opportunities for improving the use of computer vision in municipal solid waste sorting

This article explores the possibilities of improving the application of optical sorting of municipal solid waste on conveyor belts using computer vision and machine learning technologies in order to replace manual sorting lines. Also the relevance of improving waste sorting is emphasized. This work expands the understanding of the application of computer vision in the field of waste management and offers practical recommendations for its implementation in industrial processes. The paper reviews current approaches to sorting municipal solid waste, analyzes their effectiveness, and explores the potential for improving waste management processes and increasing recycling efficiency based on recent research in this area. The most popular equipment used in sorting and its features are considered. A generalization of the morphological composition of waste is carried out, and the minimum and maximum possible shares of each class of waste in the recycling stream are identified. Based on this data, sufficient proportions of images in each category are selected to train a convolutional neural network for waste classification. Applied research in terms of detection and classification of waste objects on conveyor belts using well-known neural network architectures is considered. The analysis showed that most solutions are based on 2018 architectures, while the reference classification accuracy of more modern architectures, such as OmniVec, is 17 % higher, and detection efficiency is similar orders of magnitude higher. The resulting classification accuracy is significantly affected by image augmentation in the set of images for training the neural network — its presence can increase accuracy by 10 to 15 %. The minimum training data set has been determined, which should be about 2 000 images due to the fact that after a further increase in the number, the accuracy does not change significantly.

OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction

Three-dimensional (3D) surface reconstruction is crucial to accurately detecting navel orange external qualities such as size and shape, and binocular stereo vision is promising for practical sorting scenarios due to its high frame rates and ease of deployment. For objects with texture-less surface, such as navel oranges, it is very challenging to match the corresponding points using traditional stereo matching algorithms because the detected feature points have high similarity in color as well as structure. Most of the existing deep learning-based stereo matching algorithms focus on the stereo matching performance of the whole image and lack the attention to the matching performance of the main region. Meanwhile, simple background with less texture will bring more uncertainty to the matching of corresponding points, which affects the robustness of the model. The OrangeStereo was proposed based on the GwcNet structure to achieve a better corresponding points matching effect of navel oranges, which can provide more accurate 3D surface information. The Structural Features Extraction (SFE) module was introduced after the initial feature extraction module to extract stable structural features and avoid the limitations of using single convolutional features. The Attention Weights Generation (AWG) module was utilized at matching cost calculation stage to help suppress redundant information and make the network pay more attention to the matching performance of specific regions. The improved loss function assigned a greater weight to the loss of the navel orange region using semantic information from left image to make the network more focused on the matching effect of the navel orange region. An ablation study was conducted to confirm the efficiency of the SFE module, AWG module, and the improved loss function of the algorithm. The ablation study contained five sub-experiments, including the baseline model, adding the SFE and the AWG modules respectively, and whether to add the improved loss function based on the addition of previous two modules. Additionally, PSMNet, GwcNet, RealtimeStereo, and ACVNet, four typical stereo matching methods, were contrasted with the OrangeStereo. The proposed OrangeStereo achieved better performance with EPE, RMSE, bad-1, bad-3, bad-5, and inference time of 0.60 pixels, 3.53 pixels, 2.37 %, 1.83 %, 1.62 %, and 33 ms in the orange region, respectively. The R2 and RMSE for the depth of orange utilizing our method were 0.982 and 0.81 mm, respectively. The proposed algorithm can be employed to obtain accurate depth information of fruits quickly, and it is expected to be used in the 3D reconstruction of fruits in the actual commercial fruit sorting lines to achieve more accurate external quality assessment.

Analysis of hyperspectral images of apple fruits for automated sorting

One non-invasive method for detecting fruit damage involves monitoring them using hyper-spectrometers. Hyperspectral images comprise a set of spatially resolved radiation spectra of a reflected object. By using these data, it is possible to identify the characteristics and parameters of fruits that may indicate their damage. In this study, the spectral and spatial components of hyperspectral images of apple fruits were analysed. Random forest classifiers were used to detect objects in the images, with reflection spectra, vegetation indices and spatial texture descriptors (local binary patterns) used as input data for classifiers. Classifiers based on spectral characteristics proved to be more reliable than those trained without spectral data. Using spectral information about fruits signifi cantly improved the classifi cation results under the conditions of uneven lighting interference. By combining spectral data with machine learning methods, fruit sorting efficiency can be improved. This approach offers advanced development of methods for processing data from hyperspectral sensors installed on sorting lines in order to detect damage to apple fruits reliably

An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

Vitrimeric shape memory polymer-based fingertips for adaptive grasping.

The variability in the shapes and sizes of objects presents a significant challenge for two-finger robotic grippers when it comes to manipulating them. Based on the chemistry of vitrimers (a new class of polymer materials that have dynamic covalent bonds, which allow them to reversibly change their mechanical properties under specific conditions), we present two designs as 3D-printed shape memory polymer-based shape-adaptive fingertips (SMP-SAF). The fingertips have two main properties needed for an effective grasping. First, the ability to adapt their shape to different objects. Second, exhibiting variable rigidity, to lock and retain this new shape without the need for any continuous external triggering system. Our two design strategies are: 1) A curved part, which is suitable for grasping delicate and fragile objects. In this mode and prior to gripping, the SMP-SAFs are straightened by the force of the parallel gripper and are adapted to the object by shape memory activation. 2) A straight part that takes on the form of the objects by contact force with them. This mode is better suited for gripping hard bodies and provides a more straightforward shape programming process. The SMP-SAFs can be programmed by heating them up above glass transition temperature (54°C) via Joule-effect of the integrated electrically conductive wire or by using a heat gun, followed by reshaping by the external forces (without human intervention), and subsequently fixing the new shape upon cooling. As the shape programming process is time-consuming, this technique suits adaptive sorting lines where the variety of objects is not changed from grasp to grasp, but from batch to batch.

Survey and Cost–Benefit Analysis of Sorting Technology for the Sweetpotato Packing Lines

Supplying high-quality fresh sweetpotato roots to the consumer requires sorting the roots by quality and removing culls deemed unsuitable for fresh markets at packing facilities. The sorting operation is traditionally performed by manual labor. This study surveyed the sorting lines of seven commercial sweetpotato packinghouses in Mississippi during the packing season of 2021. Sorting for defects entirely relied on labor, which accounted for up to 50% of the total labor in packinghouses. A cost–benefit analysis was conducted to determine the cost-effectiveness of implementing automated sorting technology as an alternative to manual sorting. The net benefits of automated sorting depended on labor savings and equipment costs. Machines at or less than USD 100,000 were economically beneficial with payback periods of less than three years when four or more workers could be replaced, while machines of USD 350,000 and higher would be not justifiable when quick economical returns were sought. Automated sorting promises to increase the profitability and competitiveness of fresh market sweetpotato packing industries.

An energy-efficient classification system for peach ripeness using YOLOv4 and flexible piezoelectric sensor

Peach is a kind of popular fruit with significant economic value, but it is commonly sorted through manual labor on sorting lines, which can negatively affect the economic efficiency. To address this issue, this paper proposes a system for classifying different ripeness of peaches based on their visual and tactile characteristics. This system consists of two stages. The first stage utilizes YOLOv4 as the core model for preliminary visual-based classification of fruit ripeness. The second stage proposes a method centered on flexible piezoelectric sensors to more deeply classify the fruit ripeness based on tactile characteristics. The system classifies peaches into five categories (A1, A2, B1, B2, and R), which can be applied to the peach sorting line. The mAP and meanIoU are used as evaluation parameters for the visual part and TPA test is used to validate the accuracy of the tactile part. The results of the experiments show that mAP value reaches 0.9304, meanIoU reaches 0.9454, and the accuracy of the tactile part reaches 92.22%. The results indicate that visual and tactile characteristics can accurately classify peach ripeness using YOLOv4 and flexible piezoelectric sensor. The proposed system completes the entire classification with lower cost and less power consumption, providing potential ways to increase productivity and automate the sorting line.

Mutual Augmentation of Spectral Sensing and Machine Learning for Non-Invasive Detection of Apple Fruit Damages

Non-invasive techniques for the detection of apple fruit damages are central to the correct operation of sorting lines ensuring storability of the collected fruit batches. The choice of optimal method of fruit imaging and efficient image processing method is still a subject of debate. Here, we have dissected the information content of hyperspectral images focusing on either spectral component, spatial component, or both. We have employed random forest (RF) classifiers using different parameters as inputs: reflectance spectra, vegetation indices (VIs), and spatial texture descriptors (local binary patterns, or LBP), comparing their performance in the task of damage detection in apple fruit. The amount of information in raw hypercubes was found to be over an order of magnitude excessive for the end-to-end problem of classification. Converting spectra to vegetation indices has resulted in a 60-fold compression with no significant loss of information relevant for phenotyping and more robust performance with respect to varying illumination conditions. We concluded that the advanced machine learning approaches could be more efficient if complemented by spectral information about the objects in question. We discuss the potential advantages and pitfalls of the different approaches to the machine learning-based processing of hyperspectral data for fruit grading.

Post-consumer plastic sorting infrastructure improvements planning: Scenario-based modeling of greenhouse gas savings with sustainable costs

With the increasing share of waste material recovery, household plastic waste is one of the biggest problems. In most countries, mainly manual sorting is used. Meanwhile, new automated technologies are being developed to expand the range of classifiable types to increase material recovery. The overall automation of the sorting process can help the EU's established recycling targets to be effectively met. However, the new technologies are feasible only in the case of large-capacity centers, which must be conveniently located in the existing infrastructure. This paper presents a two-stage model aiming to modernize the current sorting infrastructure for plastic waste. The approach uses multi-criteria optimization to minimize environmental impact at a reasonable price. The result is the optimal location of new automatic sorting centers, and waste stream flows using existing manual sorting facilities. The model is applied through an initial case study inspired by the Czech Republic data. Optimization output proposes four new automatic sorting lines with a total capacity of 158 kt per year. In most cases, manual sorting is used to reduce the transported weight of plastic waste, while automatic sorting lines separate the remaining, hardly recognized part. More than 60% of separately collected plastic is sorted and determined for material recovery.

Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness

Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.

A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets

Detecting and eliminating sprouted potatoes is a basic measure before potato storage, which can effectively improve the quality of potatoes before storage and reduce economic losses due to potato spoilage and decay. In this paper, we propose an improved YOLOV5-based sprouted potato detection model for detecting and grading sprouted potatoes in complex scenarios. By replacing Conv with CrossConv in the C3 module, the feature similarity loss problem of the fusion process is improved, and the feature representation is enhanced. SPP is improved using fast spatial pyramid pooling to reduce feature fusion parameters and speed up feature fusion. The 9-Mosaic data enhancement algorithm improves the model generalization ability; the anchor points are reconstructed using the genetic algorithm K-means to enhance small target features, and then multi-scale training and hyperparameter evolution mechanisms are used to improve the accuracy. The experimental results show that the improved model has 90.14% recognition accuracy and 88.1% mAP, and the mAP is 4.6%, 7.5%, and 12.4% higher compared with SSD, YOLOV5, and YOLOV4, respectively. In summary, the improved YOLOV5 model, with good detection accuracy and effectiveness, can meet the requirements of rapid grading in automatic potato sorting lines.

A Fuzzy Multi-Criteria Model for Municipal Waste Treatment Systems Evaluation including Energy Recovery

One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation.

The hype in spectral imaging

Author Summary: Hyperspectral imaging is currently a very well-known and much used technology for measuring features in different fields, such as chemistry, geology, medicine, food and agriculture, either spaceborne (satellites), airborne (drones) or at close proximity (e.g. field scanning, industrial sorting lines or microscopy). Its background is two-fold, and it can be considered as a special case of spectroscopy (“imaging spectroscopy”) or a special case of imaging (“spectral imaging”). Current practice is to use adjectives such as multi and hyper added to “spectral imaging” in order to characterise the number of wavelength bands. In this paper we propose the community to use scientifically sound terminology, like “imaging spectroscopy” or “spectral imaging”, without using ambiguous adjectives. Further, we encourage the community to define and agree upon clear adjectives to describe the number of variables in the naming of our imaging technique.

Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Toward recycling ʺunsortableʺ post-consumer WEEE stream: Characterization and impact of electron beam irradiation on mechanical properties

The work reported here is the first study aimed at providing a full screening of a real unsortable non-recycled post-consumer WEEE stream free of brominated flame retarded plastics, separated using on-line X-ray detection, toward its recycling. In the existing sorting lines, up to 40% of plastics from waste electrical and electronic equipment (WEEE) stream can be rejected, herein named unsortable plastics. To have the most representative homogeneous sample for physico-chemical characterizations, a sampling method was developed to overcome the heterogeneity of the investigated 500 kg batch. The batch screening on both representative samples (∼500 μm size) and 100 plastic fractions (∼20 mm size), by means of routine techniques used in the plastic industry, has allowed to quantify reliably the main polymers included in the studied batch; ∼50% styrene-based polymers, ∼15% polypropylene (PP), ∼15% polycarbonate (PC), ∼1–4% polyamide (PA), polyethylene (PE), polyvinyl chloride (PVC), poly (ethylene terephthalate) (PET), poly (methyl methacrylate) (PMMA) and ∼8% of multi-layer plastics, paints and thermosets. The identification of the ∼8.0% inorganic phase by X-ray fluorescence spectrometry revealed the presence of several additives/charges commonly incorporated in plastic materials, such as calcium carbonate and talc. The studied batch was then subjected to electron beam irradiation at 50 and 200 kGy doses, as a means of compatibilization between the batch components. The mechanical properties and thermal behavior of irradiated samples pointed out the crucial role of the residual free radical scavenger agents present in post-consumer WEEE streams, leading to significantly different properties compared to those of irradiated virgin polymer blends highlighted in the literature.

Strategic decisions leading to sustainable waste management: Separation, sorting and recycling possibilities

A circular economy is a trendy word, which encompasses reaching of closed-loop in all industries. This also applies to the waste management sector, where new regulations and directives have been issued. Given milestones, however, do not have to correspond with economic or even environmental sustainability. Thus, it is more than necessary to evaluate waste management system using these criteria and present supportive or contradictory statements. An approach for such a complex assessment is proposed. It uses mathematical programming models and multi-objective strategy to suggest a suitable number and locations for waste treatment and pre-treatment facilities. Only main fractions of municipal solid waste are considered – plastic, paper, glass and mixed municipal waste. The environmental part covers the production of greenhouse gases respecting all processes in the system, excluding transportation. Based on comprehensive research, non-linear dependencies were estimated for costs and sorting efficiencies. The mathematical model uses an iterative procedure to avoid non-linearity. Afterwards, the case study of Czech treatment infrastructure is designed. The approach resulted in the establishment of 14, and 42 sorting lines for plastics and paper, respectively, supported by 20 Waste-to-Energy plants with different capacity. An interesting output says that in an optimal situation, out of total sorted plastics and paper, only 25% and 79% will be recycled, while the remaining amount ends up in Waste-to-Energy plant. Further research will include additional fractions, such as bio-waste, oils or textile, as it is being collected these days separately. Other directions involve new targets from the real-operated sorting lines and new conditions related to investment for more fractions treated at a single site.